---
name: geomaster
description: Comprehensive geospatial science skill covering remote sensing, GIS, spatial analysis, machine learning for earth observation, and 30+ scientific domains. Supports satellite imagery processing (Sentinel, Landsat, MODIS, SAR, hyperspectral), vector and raster data operations, spatial statistics, point cloud processing, network analysis, and 7 programming languages (Python, R, Julia, JavaScript, C++, Java, Go) with 500+ code examples. Use for remote sensing workflows, GIS analysis, spatial ML, Earth observation data processing, terrain analysis, hydrological modeling, marine spatial analysis, atmospheric science, and any geospatial computation task.
license: MIT License
metadata:
    skill-author: K-Dense Inc.
---

# GeoMaster

GeoMaster is a comprehensive geospatial science skill covering the full spectrum of geographic information systems, remote sensing, spatial analysis, and machine learning for Earth observation. This skill provides expert knowledge across 70+ topics with 500+ code examples in 7 programming languages.

## Installation

### Core Python Geospatial Stack

```bash
# Install via conda (recommended for geospatial dependencies)
conda install -c conda-forge gdal rasterio fiona shapely pyproj geopandas

# Or via uv
uv pip install geopandas rasterio fiona shapely pyproj
```

### Remote Sensing & Image Processing

```bash
# Core remote sensing libraries
uv pip install rsgislib torchgeo eo-learn

# For Google Earth Engine
uv pip install earthengine-api

# For SNAP integration
# Download from: https://step.esa.int/main/download/
```

### GIS Software Integration

```bash
# QGIS Python bindings (usually installed with QGIS)
# ArcPy requires ArcGIS Pro installation

# GRASS GIS
conda install -c conda-forge grassgrass

# SAGA GIS
conda install -c conda-forge saga-gis
```

### Machine Learning for Geospatial

```bash
# Deep learning for remote sensing
uv pip install torch-geometric tensorflow-caney

# Spatial machine learning
uv pip install libpysal esda mgwr
uv pip install scikit-learn xgboost lightgbm
```

### Point Cloud & 3D

```bash
# LiDAR processing
uv pip install laspy pylas

# Point cloud manipulation
uv pip install open3d pdal

# Photogrammetry
uv pip install opendm
```

### Network & Routing

```bash
# Street network analysis
uv pip install osmnx networkx

# Routing engines
uv pip install osrm pyrouting
```

### Visualization

```bash
# Static mapping
uv pip install cartopy contextily mapclassify

# Interactive web maps
uv pip install folium ipyleaflet keplergl

# 3D visualization
uv pip install pydeck pythreejs
```

### Big Data & Cloud

```bash
# Distributed geospatial processing
uv pip install dask-geopandas

# Xarray for multidimensional arrays
uv pip install xarray rioxarray

# Planetary Computer
uv pip install pystac-client planetary-computer
```

### Database Support

```bash
# PostGIS
conda install -c conda-forge postgis

# SpatiaLite
conda install -c conda-forge spatialite

# GeoAlchemy2 for SQLAlchemy
uv pip install geoalchemy2
```

### Additional Programming Languages

```bash
# R geospatial packages
# install.packages(c("sf", "terra", "raster", "terra", "stars"))

# Julia geospatial packages
# import Pkg; Pkg.add(["ArchGDAL", "GeoInterface", "GeoStats.jl"])

# JavaScript (Node.js)
# npm install @turf/turf terraformer-arcgis-parser

# Java
# Maven: org.geotools:gt-main
```

## Quick Start

### Reading Satellite Imagery and Calculating NDVI

```python
import rasterio
import numpy as np

# Open Sentinel-2 imagery
with rasterio.open('sentinel2.tif') as src:
    # Read red (B04) and NIR (B08) bands
    red = src.read(4)
    nir = src.read(8)

    # Calculate NDVI
    ndvi = (nir.astype(float) - red.astype(float)) / (nir + red)
    ndvi = np.nan_to_num(ndvi, nan=0)

    # Save result
    profile = src.profile
    profile.update(count=1, dtype=rasterio.float32)

    with rasterio.open('ndvi.tif', 'w', **profile) as dst:
        dst.write(ndvi.astype(rasterio.float32), 1)

print(f"NDVI range: {ndvi.min():.3f} to {ndvi.max():.3f}")
```

### Spatial Analysis with GeoPandas

```python
import geopandas as gpd

# Load spatial data
zones = gpd.read_file('zones.geojson')
points = gpd.read_file('points.geojson')

# Ensure same CRS
if zones.crs != points.crs:
    points = points.to_crs(zones.crs)

# Spatial join (points within zones)
joined = gpd.sjoin(points, zones, how='inner', predicate='within')

# Calculate statistics per zone
stats = joined.groupby('zone_id').agg({
    'value': ['count', 'mean', 'std', 'min', 'max']
}).round(2)

print(stats)
```

### Google Earth Engine Time Series

```python
import ee
import pandas as pd

# Initialize Earth Engine
ee.Initialize(project='your-project-id')

# Define region of interest
roi = ee.Geometry.Point([-122.4, 37.7]).buffer(10000)

# Get Sentinel-2 collection
s2 = (ee.ImageCollection('COPERNICUS/S2_SR_HARMONIZED')
      .filterBounds(roi)
      .filterDate('2020-01-01', '2023-12-31')
      .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE', 20)))

# Add NDVI band
def add_ndvi(image):
    ndvi = image.normalizedDifference(['B8', 'B4']).rename('NDVI')
    return image.addBands(ndvi)

s2_ndvi = s2.map(add_ndvi)

# Extract time series
def extract_series(image):
    stats = image.reduceRegion(
        reducer=ee.Reducer.mean(),
        geometry=roi.centroid(),
        scale=10,
        maxPixels=1e9
    )
    return ee.Feature(None, {
        'date': image.date().format('YYYY-MM-dd'),
        'ndvi': stats.get('NDVI')
    })

series = s2_ndvi.map(extract_series).getInfo()
df = pd.DataFrame([f['properties'] for f in series['features']])
df['date'] = pd.to_datetime(df['date'])
print(df.head())
```

## Core Concepts

### Coordinate Reference Systems (CRS)

Understanding CRS is fundamental to geospatial work:

- **Geographic CRS**: EPSG:4326 (WGS 84) - uses lat/lon degrees
- **Projected CRS**: EPSG:3857 (Web Mercator) - uses meters
- **UTM Zones**: EPSG:326xx (North), EPSG:327xx (South) - minimizes distortion

See [coordinate-systems.md](references/coordinate-systems.md) for comprehensive CRS reference.

### Vector vs Raster Data

**Vector Data**: Points, lines, polygons with discrete boundaries
- Shapefiles, GeoJSON, GeoPackage, PostGIS
- Best for: administrative boundaries, roads, infrastructure

**Raster Data**: Grid of cells with continuous values
- GeoTIFF, NetCDF, HDF5, COG
- Best for: satellite imagery, elevation, climate data

### Spatial Data Types

| Type | Examples | Libraries |
|------|----------|-----------|
| Vector | Shapefiles, GeoJSON, GeoPackage | GeoPandas, Fiona, GDAL |
| Raster | GeoTIFF, NetCDF, IMG | Rasterio, GDAL, Xarray |
| Point Cloud | LAZ, LAS, PCD | Laspy, PDAL, Open3D |
| Topology | TopoJSON, TopoArchive | TopoJSON, NetworkX |
| Spatiotemporal | Trajectories, Time-series | MovingPandas, PyTorch Geometric |

### OGC Standards

Key Open Geospatial Consortium standards:
- **WMS**: Web Map Service - raster maps
- **WFS**: Web Feature Service - vector data
- **WCS**: Web Coverage Service - raster coverage
- **WPS**: Web Processing Service - geoprocessing
- **WMTS**: Web Map Tile Service - tiled maps

## Common Operations

### Remote Sensing Operations

#### Spectral Indices Calculation

```python
import rasterio
import numpy as np

def calculate_indices(image_path, output_path):
    """Calculate NDVI, EVI, SAVI, and NDWI from Sentinel-2."""
    with rasterio.open(image_path) as src:
        # Read bands: B2=Blue, B3=Green, B4=Red, B8=NIR, B11=SWIR1
        blue = src.read(2).astype(float)
        green = src.read(3).astype(float)
        red = src.read(4).astype(float)
        nir = src.read(8).astype(float)
        swir1 = src.read(11).astype(float)

        # Calculate indices
        ndvi = (nir - red) / (nir + red + 1e-8)
        evi = 2.5 * (nir - red) / (nir + 6*red - 7.5*blue + 1)
        savi = ((nir - red) / (nir + red + 0.5)) * 1.5
        ndwi = (green - nir) / (green + nir + 1e-8)

        # Stack and save
        indices = np.stack([ndvi, evi, savi, ndwi])
        profile = src.profile
        profile.update(count=4, dtype=rasterio.float32)

        with rasterio.open(output_path, 'w', **profile) as dst:
            dst.write(indices)

# Usage
calculate_indices('sentinel2.tif', 'indices.tif')
```

#### Image Classification

```python
from sklearn.ensemble import RandomForestClassifier
import geopandas as gpd
import rasterio
from rasterio.features import rasterize
import numpy as np

def classify_imagery(raster_path, training_gdf, output_path):
    """Train Random Forest classifier and classify imagery."""
    # Load imagery
    with rasterio.open(raster_path) as src:
        image = src.read()
        profile = src.profile
        transform = src.transform

    # Extract training data
    X_train, y_train = [], []

    for _, row in training_gdf.iterrows():
        mask = rasterize(
            [(row.geometry, 1)],
            out_shape=(profile['height'], profile['width']),
            transform=transform,
            fill=0,
            dtype=np.uint8
        )
        pixels = image[:, mask > 0].T
        X_train.extend(pixels)
        y_train.extend([row['class_id']] * len(pixels))

    X_train = np.array(X_train)
    y_train = np.array(y_train)

    # Train classifier
    rf = RandomForestClassifier(n_estimators=100, max_depth=20, n_jobs=-1)
    rf.fit(X_train, y_train)

    # Predict full image
    image_reshaped = image.reshape(image.shape[0], -1).T
    prediction = rf.predict(image_reshaped)
    prediction = prediction.reshape(profile['height'], profile['width'])

    # Save result
    profile.update(dtype=rasterio.uint8, count=1)
    with rasterio.open(output_path, 'w', **profile) as dst:
        dst.write(prediction.astype(rasterio.uint8), 1)

    return rf
```

### Vector Operations

```python
import geopandas as gpd
from shapely.ops import unary_union

# Buffer analysis
gdf['buffer_1km'] = gdf.geometry.to_crs(epsg=32633).buffer(1000)

# Spatial relationships
intersects = gdf[gdf.geometry.intersects(other_geometry)]
contains = gdf[gdf.geometry.contains(point_geometry)]

# Geometric operations
gdf['centroid'] = gdf.geometry.centroid
gdf['convex_hull'] = gdf.geometry.convex_hull
gdf['simplified'] = gdf.geometry.simplify(tolerance=0.001)

# Overlay operations
intersection = gpd.overlay(gdf1, gdf2, how='intersection')
union = gpd.overlay(gdf1, gdf2, how='union')
difference = gpd.overlay(gdf1, gdf2, how='difference')
```

### Terrain Analysis

```python
import rasterio
from rasterio.features import shapes
import numpy as np

def calculate_terrain_metrics(dem_path):
    """Calculate slope, aspect, hillshade from DEM."""
    with rasterio.open(dem_path) as src:
        dem = src.read(1)
        transform = src.transform

    # Calculate gradients
    dy, dx = np.gradient(dem)

    # Slope (in degrees)
    slope = np.arctan(np.sqrt(dx**2 + dy**2)) * 180 / np.pi

    # Aspect (in degrees, clockwise from north)
    aspect = np.arctan2(-dy, dx) * 180 / np.pi
    aspect = (90 - aspect) % 360

    # Hillshade
    azimuth = 315
    altitude = 45
    azimuth_rad = np.radians(azimuth)
    altitude_rad = np.radians(altitude)

    hillshade = (np.sin(altitude_rad) * np.sin(np.radians(slope)) +
                 np.cos(altitude_rad) * np.cos(np.radians(slope)) *
                 np.cos(np.radians(aspect) - azimuth_rad))

    return slope, aspect, hillshade
```

### Network Analysis

```python
import osmnx as ox
import networkx as nx

# Download street network
G = ox.graph_from_place('San Francisco, CA', network_type='drive')

# Add speeds and travel times
G = ox.add_edge_speeds(G)
G = ox.add_edge_travel_times(G)

# Find shortest path
orig_node = ox.distance.nearest_nodes(G, -122.4, 37.7)
dest_node = ox.distance.nearest_nodes(G, -122.3, 37.8)
route = nx.shortest_path(G, orig_node, dest_node, weight='travel_time')

# Calculate accessibility
accessibility = {}
for node in G.nodes():
    subgraph = nx.ego_graph(G, node, radius=5, distance='time')
    accessibility[node] = len(subgraph.nodes())
```

## Detailed Documentation

Comprehensive reference documentation is organized by topic:

- **[Core Libraries](references/core-libraries.md)** - GDAL, Rasterio, Fiona, Shapely, PyProj, GeoPandas fundamentals
- **[Remote Sensing](references/remote-sensing.md)** - Satellite missions, optical/SAR/hyperspectral analysis, image processing
- **[GIS Software](references/gis-software.md)** - QGIS/PyQGIS, ArcGIS/ArcPy, GRASS, SAGA integration
- **[Scientific Domains](references/scientific-domains.md)** - Marine, atmospheric, hydrology, agriculture, forestry applications
- **[Advanced GIS](references/advanced-gis.md)** - 3D GIS, spatiotemporal analysis, topology, network analysis
- **[Programming Languages](references/programming-languages.md)** - R, Julia, JavaScript, C++, Java, Go geospatial tools
- **[Machine Learning](references/machine-learning.md)** - Deep learning for RS, spatial ML, GNNs, XAI for geospatial
- **[Big Data](references/big-data.md)** - Distributed processing, cloud platforms, GPU acceleration
- **[Industry Applications](references/industry-applications.md)** - Urban planning, disaster management, precision agriculture
- **[Specialized Topics](references/specialized-topics.md)** - Geostatistics, optimization, ethics, best practices
- **[Data Sources](references/data-sources.md)** - Satellite data catalogs, open data repositories, API access
- **[Code Examples](references/code-examples.md)** - 500+ code examples across 7 programming languages

## Common Workflows

### End-to-End Land Cover Classification

```python
import rasterio
import geopandas as gpd
from sklearn.ensemble import RandomForestClassifier
import numpy as np

# 1. Load training data
training = gpd.read_file('training_polygons.gpkg')

# 2. Load satellite imagery
with rasterio.open('sentinel2.tif') as src:
    bands = src.read()
    profile = src.profile
    meta = src.meta

# 3. Extract training pixels
X, y = [], []
for _, row in training.iterrows():
    mask = rasterize_features(row.geometry, profile['shape'])
    pixels = bands[:, mask > 0].T
    X.extend(pixels)
    y.extend([row['class']] * len(pixels))

# 4. Train model
model = RandomForestClassifier(n_estimators=100, max_depth=20)
model.fit(X, y)

# 5. Classify image
pixels_reshaped = bands.reshape(bands.shape[0], -1).T
prediction = model.predict(pixels_reshaped)
classified = prediction.reshape(bands.shape[1], bands.shape[2])

# 6. Save result
profile.update(dtype=rasterio.uint8, count=1, nodata=255)
with rasterio.open('classified.tif', 'w', **profile) as dst:
    dst.write(classified.astype(rasterio.uint8), 1)

# 7. Accuracy assessment (with validation data)
# ... (see references for complete workflow)
```

### Flood Hazard Mapping Workflow

```python
# 1. Download DEM (e.g., from ALOS AW3D30, SRTM, Copernicus)
# 2. Process DEM: fill sinks, calculate flow direction
# 3. Define flood scenarios (return periods)
# 4. Hydraulic modeling (HEC-RAS, LISFLOOD)
# 5. Generate inundation maps
# 6. Assess exposure (settlements, infrastructure)
# 7. Calculate damage estimates

# See references/hydrology.md for complete implementation
```

### Time Series Analysis for Vegetation Monitoring

```python
import ee
import pandas as pd
import matplotlib.pyplot as plt

# Initialize GEE
ee.Initialize(project='your-project')

# Define ROI
roi = ee.Geometry.Point([x, y]).buffer(5000)

# Get Landsat collection
landsat = ee.ImageCollection('LANDSAT/LC08/C02/T1_L2')\
    .filterBounds(roi)\
    .filterDate('2015-01-01', '2024-12-31')\
    .filter(ee.Filter.lt('CLOUD_COVER', 20))

# Calculate NDVI time series
def add_ndvi(img):
    ndvi = img.normalizedDifference(['SR_B5', 'SR_B4']).rename('NDVI')
    return img.addBands(ndvi)

landsat_ndvi = landsat.map(add_ndvi)

# Extract time series
ts = landsat_ndvi.getRegion(roi, 30).getInfo()
df = pd.DataFrame(ts[1:], columns=ts[0])
df['date'] = pd.to_datetime(df['time'])

# Analyze trends
from scipy import stats
slope, intercept, r_value, p_value, std_err = stats.linregress(
    range(len(df)), df['NDVI']
)

print(f"Trend: {slope:.6f} NDVI/year (p={p_value:.4f})")
```

### Multi-Criteria Suitability Analysis

```python
import geopandas as gpd
import rasterio
import numpy as np
from sklearn.preprocessing import MinMaxScaler

# 1. Load criteria rasters
criteria = {
    'slope': rasterio.open('slope.tif').read(1),
    'distance_to_water': rasterio.open('water_dist.tif').read(1),
    'soil_quality': rasterio.open('soil.tif').read(1),
    'land_use': rasterio.open('landuse.tif').read(1)
}

# 2. Reclassify (lower is better for slope/distance)
weights = {'slope': 0.3, 'distance_to_water': 0.2,
           'soil_quality': 0.3, 'land_use': 0.2}

# 3. Normalize (0-1, using fuzzy membership)
normalized = {}
for key, raster in criteria.items():
    if key in ['slope', 'distance_to_water']:
        # Decreasing suitability
        normalized[key] = 1 - MinMaxScaler().fit_transform(raster.reshape(-1, 1))
    else:
        normalized[key] = MinMaxScaler().fit_transform(raster.reshape(-1, 1))

# 4. Weighted overlay
suitability = sum(normalized[key] * weights[key] for key in criteria)
suitability = suitability.reshape(criteria['slope'].shape)

# 5. Classify suitability levels
# (Low, Medium, High, Very High)

# 6. Save result
profile = rasterio.open('slope.tif').profile
profile.update(dtype=rasterio.float32, count=1)
with rasterio.open('suitability.tif', 'w', **profile) as dst:
    dst.write(suitability.astype(rasterio.float32), 1)
```

## Performance Tips

1. **Use Spatial Indexing**: R-tree indexes speed up spatial queries by 10-100x
   ```python
   gdf.sindex  # Automatically created by GeoPandas
   ```

2. **Chunk Large Rasters**: Process in blocks to avoid memory errors
   ```python
   with rasterio.open('large.tif') as src:
       for window in src.block_windows():
           block = src.read(window=window)
   ```

3. **Use Dask for Big Data**: Parallel processing on large datasets
   ```python
   import dask.array as da
   dask_array = da.from_rasterio('large.tif', chunks=(1, 1024, 1024))
   ```

4. **Enable GDAL Caching**: Speed up repeated reads
   ```python
   import gdal
   gdal.SetCacheMax(2**30)  # 1GB cache
   ```

5. **Use Arrow for I/O**: Faster file reading/writing
   ```python
   gdf.to_file('output.gpkg', use_arrow=True)
   ```

6. **Reproject Once**: Do all analysis in a single projected CRS
7. **Use Efficient Formats**: GeoPackage > Shapefile, Parquet for large datasets
8. **Simplify Geometries**: Reduce complexity when precision isn't critical
   ```python
   gdf['geometry'] = gdf.geometry.simplify(tolerance=0.0001)
   ```

9. **Use COG for Cloud**: Cloud-Optimized GeoTIFF for remote data
10. **Enable Parallel Processing**: Most libraries support n_jobs=-1

## Best Practices

1. **Always Check CRS** before any spatial operation
   ```python
   assert gdf1.crs == gdf2.crs, "CRS mismatch!"
   ```

2. **Use Appropriate CRS**:
   - Geographic (EPSG:4326) for global data, storage
   - Projected (UTM) for area/distance calculations
   - Web Mercator (EPSG:3857) for web mapping only

3. **Validate Geometries** before operations
   ```python
   gdf = gdf[gdf.is_valid]
   gdf['geometry'] = gdf.geometry.make_valid()
   ```

4. **Handle Missing Data** appropriately
   ```python
   gdf['geometry'] = gdf['geometry'].fillna(None)
   ```

5. **Document Projections** in metadata
6. **Use Vector Tiles** for web maps with many features
7. **Apply Cloud Masking** for optical imagery
8. **Calibrate Radiometric Values** for quantitative analysis
9. **Preserve Lineage** for reproducible research
10. **Use Appropriate Spatial Resolution** for your analysis scale